It is known in the art to compress a database containing data to minimize storage requirements for storing the data and reduce transmission times for transmitting the data. In prior approaches, the entire database is compressed and decompressed or extracted for manipulation/query of the data in the database. For example, prior approaches are directed to reducing the search time required for searching over a large database using methods such as binary searches or b-trees both of which require that the data in the database can be read randomly. In order to support random reading from and writing to a compressed database, the entire database must be decompressed.
There is a need in the art for a database having a compressed data structure enabling update of the data without requiring decompression of the entire database. That is, the database remains compressed and occupies a smaller storage space thereby requiring less memory and less transmission time to transfer the database update contents.
For example, handheld or embedded devices are constrained by limited processing power and limited storage or memory in order to increase the device's battery life. A method of updating a compressed database would enable a larger amount of data to be stored on the device and would increase update time. However, prior approaches have always decompressed the entirety of the data prior to update of the data on the device thereby eliminating any advantage gained from database compression.